The ice factory of Hudson Bay
Bibliographic record
Abstract
Within the dynamic seasonal ice cover of Hudson Bay, the Kivalliq Polynya is a large latent heat polynya that forms throughout winter in the northwest as a result of strong northwesterly offshore surface winds. Polynyas are known to be physically, biologically, and geochemically important and contribute to the regional ice mass balance; however, the Kivalliq Polynya has yet to be characterized in terms of spatiotemporal variability and ice production. Using a thin ice algorithm applied to the 16-year record of daily AMSR-E and AMSR-2 passive microwave observations, we examine the interannual variability in the spatial and temporal characteristics of the polynya throughout winter (December–April) over the period 2002–2019. Our study reveals that the polynya is present in some form almost every day but that its daily area is highly variable. On average, 182 km3 of new ice is produced in the Kivalliq Polynya during winter, or approximately 20% of the end of winter ice volume in Hudson Bay. Daily ice production is found to be significantly correlated with the daily polynya area, though large, episodic events can increase annual cumulative ice production during a year of otherwise small polynyas. Annual cumulative ice production is also found to be significantly correlated with seasonally averaged offshore wind speeds, which explain 47.3% of the variance in winter ice production and drive a 46 km3 increase in ice production for every 1.0 m s–1 increase in offshore winds. Ultimately, the highly variable yet persistent Kivalliq Polynya is shown to be driven by offshore winds and significantly contributes to the regional ice mass balance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.063 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".